def __init__(self, x, y):
self.xFile = []
self.yFile = '{}'.format(y[0])
self.length = 0
self.volume = 0
self.peak = []
self.filterSignal = []
i = 0
while i < len(x):
self.xFile.append('{}'.format(x[i]))
i += 1
self.title = '{}'.format(namespace.title)
self.output = '{}'.format(namespace.output)
self.dataoutput = '{}'.format(namespace.dataoutput)
self.dataStressFile = '{}'.format(namespace.dataStressFile)
self.dataDir = '{}'.format(namespace.dataDir)
self.distanceDump = '{}'.format(namespace.distanceDump)
self.distanceStat = '{}'.format(namespace.distanceStat)
self.xlabel = '{}'.format(namespace.xlabel)
self.ylabel = '{}'.format(namespace.ylabel)
self.l1 = '{}'.format(namespace.l1)
self.l2 = '{}'.format(namespace.l2)
self.l3 = '{}'.format(namespace.l3)
self.time = np.array(getColumn(self.yFile, 0)).astype(float) * 0.001
self.sxx = np.array(getColumn(self.yFile, 1)).astype(float)
self.syy = np.array(getColumn(self.yFile, 2)).astype(float)
self.szz = np.array(getColumn(self.yFile, 3)).astype(float)
if namespace.toforce:
self.sxx *= np.pi / 100
self.syy *= np.pi / 100
self.szz *= np.pi / 100
self.computeDist()
self.sxx /= self.volume
self.syy /= self.volume
self.szz /= self.volume
if namespace.sumfilter:
self.filterSignal = noiseFilter(self.syy, 5, 75)
if namespace.plotpeaks:
self.peakDetect(self.filterSignal, 12000, 40, 150, 5950)
if namespace.writedata:
self.writeToFile()
if namespace.stdout and namespace.plotpeaks:
self.stdOut()
if namespace.dataStressFile:
self.writeStressFile()
self.computeDistAnalizeAtoms()
def noiseFilterPlot(time, s):
v = noiseFilter(s, 5, 100)
plt.plot(time, v, 'k')
# plt.plot(time[imax], vmax, 'c.', label=' %.2f' % (vmax))
return v
def noiseFilterPlot(xs,s): v = noiseFilter(s,5,80) vavg = np.mean(v[ 0:( len(v) / 6 ) ]) plt.plot(xs,v,'k', label=' %.2f'%(vavg))